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3 Materials and Methods
3.1 Study area
RISCO-QB project was held in the Quiberon Bay, which is an open bay located in southern Brittany Figure 16. The bay is located in the southeast of the
Armorican Massif on the northern part of the Armorican oceanic margin between Long. 3
o
10 – 2
o
55 W and Lat. 47
o
34 - 47
o
24 N.
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Figure 16.a. Chart of France, b. Quiberon Bay Menier, et. al., 2010
3.2 Data Source
This research used RISCO data. RISCO project is conducted by Ifremer with their partners. The data collected monthly from April to December 2010.
3.3 Required Tools
Several hardware and software used during this research such are personal computer and printer, geographic information system software ArcGis version
10, The R project for statistical computing Venables et. al, 2010 and also office application software Libre Office for windows.
3.4 Methodology 3.4.1 Determine of Study Area
As present in the study area, this project is held in Quiberon Bay, France and the 15 station’s of oyster culture as we can see in figure 18.
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The Quiberon Bay is dominated by sand with muddy sediment in the deeper zone Figure 18.
Martin 1978 observed that minimal temperature average 7-10
O
C in the winter, and maximal temperature 20-22
O
C in the summer. The value of salinity is variable within the year, between 30 and 35.
Figure 17. Risco Stations
Figure 18.Sedimentology of Quiberon Bay Menier, 2010
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3.4.2 Data collecting
In analyzing and discussing the issues in this research requires data that consists of two data sources, namely:
primary data and secondary data.
1.
Primary data is collected by the investigator through field survey. Such data are in raw form and must be refined before use. These data can be
gathered internally or externally though surveys, observations, experiments, and simulation.
2.
Secondary data is extracted from the existing published or unpublished sources, that is from the data already collected by others. Before the use
of secondary data, in example, other persons data, we must properly scrutinize and edit them to find whether these data are: reliable,
suitable, and adequate.
3.4.3 Processing of Risco Data
The experimental research is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls
and measures any change in other variables. Experiments are conducted to be able to predict phenomenons. Typically, an experiment is constructed to be able to
explain some kind of causation. The case study is the most flexible of all research designs, allowing the researcher to retain the holistic characteristics of real-life
events while investigating empirical events Yin, 1984. There was several experiment design in RISCO project, in every station;
we have a cage of oyster which is showed in figure 19.
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The observations of this research have been conducted monthly 8 surveys in May until December 2010, which are:
1. 3 and 4 May 2010 2. 31 May and 1 June 2010
3. 5 and 6 July 2010 4. 2 and 3 August 2010
5. 30 and 31 August 2010 6. 27 and 28 September 2010
7. 4 and 5 November 2010 8. 13 and 14 December 2010
Each month, there are several analyses which have been done, such as: 1. Analysis of pathology OsHV
1
and vibrio 2. Analysis of sediment granulometry
3. Analysis of hydrology such as temperature, salinity, oxygen, chlorophyll a, turbidity and suspended matter, and
4. Oyster growth and mortality Spat and Adult
Here, we focus on the fourth, especially on mortality of oyster. In each
station, we counted the number of predators starfishes and gastropods on the
Figure 19. Experimental Design of Oyster Culture
Surface water
On-bottom of the sea
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cage and in each bag we calculated the numbers of dead oysters then we took sampling of 30 living oysters for growth and pathology analysis. In each dead
oyster, we also observed the boring oyster by oyster drill and parasitism by Polydora. We did the same thing from April to December 2010
and sometimes we added several oysters at each station, in case of there was huge death oysters,
approximately 50-100 oysters.
4 Results
4.1 The Predators in the Quiberon Bay
The presence of predators in Quiberon Bay from May to December 2010 shows in figure 21. Two types of predator have been observed in this study, they
are gastropods Ocenebra erinacea and Ocinebrillus inornatus and starfishes Asterias rubens and Marthasterias glacialis.
In distribution of gastropods, there were three clusters: the first class number of gastropod was 32 in red color, the second class number of
gastropod was 32-100 in green color, and the last group number of gastropod was 100 in blue color. There was highest abundance of gastropod in station 15
in 4 November 2010 which was contain 342 specimens. In the case of starfishes three classes can be also defined: the first class number of starfish was ≤ 2, in
green color, the second class number of starfish was 3-8, in red color, and the last number of starfish 8 in blue color. In the below of figure 21 shows the
mean density of the predator per station, 68 gastropods in station 15 and 6 starfishes in station 2.
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Figure 20. Station RISCO
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Figure 21. a. The presence of Gastropod in Quiberon Bay, b. The presence of Starfish in Quiberon Bay
b. Starfish Average
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Whole data statistic that used in this research could be found in the appendix. Two maximum mean densities of predators have been observed in May
for Starfishes and in November for Gastropods Figure 22.
Figure 22. Mean density of gastropod and starfish per month
In table 4 and table 5, there are the variance analysis between the presence of gastropods and starfishes. First, table 4 shows that the presence of gastropods is
very significant in time and the station.
Table 4. Variance analysis of gastropods
Source Sum of
Squares Df
Mean Square
F- Ratio
P-Value month
22609.7 7
3229.96 3.53
0.0020 Station
39095.1 14
2792.5 3.05
0.0006 Residual
89756.2 98
915.879 Total corrected 151461.
11 9
:very significant
In table 5 shows the presence of starfish is very significant in time and station too.
Table 5. Variance analysis of Starfishes
Source Sum of Squares Df
Mean Square F-Ratio P-Value month
76.2583 7
10.894 2.93
0.0080 station
324.717 14
23.194 6.23
0.0000 Residual
364.617 98
3.72058 Total corrected 765.592
119 :very significant
In both tables, it showed that the value of probability ANOVA for gastropods and starfishes are below 0.05. It means that there are significant relations between
gastropod and the variables time and station in the confidence level 95.
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4.2 Oyster’s mortality The oyster mortality of adult in the on-bottom culture without protection
BAP adult, is higher in the deeper zone Figure 23.
Figure 23. Oyster’s mortality in on-bottom culture BAP adult
Four clusters can be differentiated: the first class number of oyster mortality was ≤ 10 in red color, the second class number of oyster mortality
was 11-25 in green color, the third class number of oyster mortality was 26- 65, in blue sky color and the last group number of oyster mortality was 65-
100 in dark navy color. Matrix in the below, Figure 24, shows the percentage of oyster mortality in time and station.
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Figure 24. Matrix of BAP
This matrix shows that station 2 and 5 have the highest mortalities and without time effect. Almost 100 oysters are died in this zone. On the contrary, stations 1,
4, 6, 7 and 9 have the lowest mortalities.
Table 6. Variance analysis of BAP
Source Sum of Squares
Df Mean Square
F-Ratio P-Value
month 0.680203
7 0.0971719
2.34 0.0300
station 11.1018
14 0.792987
19.10 0.0000
Residual 3.98671
96 0.0415282
Total corrected 16.0269
117
:very significant
Variance analysis Table 6 indicated that percentages of oyster mortalities on BAP are significantly different in time and in space. These data can be compared
with the distribution of oyster mortalities of BSP on-bottom, in protection-bag of predation Figure 25.
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Figure 25. Oyster mortality in on-bottom culture BSP adult
The first class number of oyster mortality was ≤ 20 in red color, the second class number of oyster mortality was 21 - 40 in green color, the third
class number of oyster mortality was ≥ 41, in dark navy color. The station 15 is characterized by the highest oyster mortality. Then, for differentiate the oyster
mortality in time and station as shows in the matrix of figure 26.
Figure 26. Matrix of BSP adult
3 and 4 May 31 and 1 June
13 and 14 Dec 4 and 5 Nov
27 and 28 Sept 5 and 6 July
30 and 31 August 2 and 3 August
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This matrix shows that the highest oyster mortality was happened in the station 15. If we make a cluster 50 the similarity based on station in this
matrix, we will find 3 clusters, which are first group consist of station 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, and 14 smaller of mortality oysters, second group is
station 2, and last group is station 15. However, we can’t cluster oyster mortalities based on time month. The variance analysis of BSP adult as shown in table 7.
Table 7. Variance analysis of BSP
Source Sum of Squares
Df Mean Square
F-Ratio P-Value
month 0.0444764
7 0.00635377
1.46 0.1909
station 0.308727
14 0.0220519
5.07 0.0000
Residual 0.417884
96 0.00435296
Total corrected 0.775067
117
:very significant
Based on table 7, the statistical significance of each factor is different for each variable. Notice that the highest P-value is 0.4104, belonging to time. The P-
value is greater or equal to 0.05, that term is not statistically significant at the 95.0 confidence level. In contrary, P-value for station is inferior to 0.05, that
term means that the oyster mortalities are significant with the station.
4.3 Oyster’s mortality by predation
Kategori